skfolio.preprocessing.CSPercentileRankScaler#

class skfolio.preprocessing.CSPercentileRankScaler(*, min_group_size=8)[source]#

Cross-sectional percentile rank.

Computes the percentile rank of each finite value within an observation’s cross-section.

When cs_weights is provided, percentile ranks are estimated only on the estimation universe, defined by cs_weights > 0. Assets outside that universe still receive percentile ranks relative to it. For this estimator, cs_weights is used only to define the estimation universe; percentile estimation itself remains equal-weighted over the selected assets.

NaNs are treated as missing values. They are ignored when computing cross-sectional ranks and are preserved in the output.

When cs_groups is None, ranks are computed globally within each observation using the formula:

\[p_{t,i} = \frac{r_{t,i} - 0.5}{N_{\mathcal{E}_t}}\]

where \(\mathcal{E}_t\) is the estimation universe at observation \(t\), \(N_{\mathcal{E}_t}\) its size, and \(r_{t,i} \in [1, N_{\mathcal{E}_t}]\) is the rank of asset \(i\) within that universe. Tied values share the average of the ranks they would otherwise occupy (equivalent to scipy.stats.rankdata(method="average")).

The \(-0.5\) shift centers the rank inside its bin, so percentiles sit strictly in \((0, 1)\), on the closed interval \([0.5 / N_{\mathcal{E}_t},\, 1 - 0.5 / N_{\mathcal{E}_t}]\). This keeps downstream inverse-normal mappings always finite.

When cs_groups is provided, the same ranking scheme is applied within each group. Groups with fewer than min_group_size estimation assets, and missing groups (cs_groups == -1), fall back to the global cross-section.

This transformer is stateless.

Parameters:
min_group_sizeint, default=8

Minimum number of estimation assets required in a group. Smaller groups fall back to the global cross-section.

Methods

fit(X[, y, cs_weights, cs_groups])

Fit the transformer.

fit_transform(X[, y, cs_weights, cs_groups])

Fit to X and return the transformed values.

get_feature_names_out([input_features])

Get output feature names for transformation.

get_metadata_routing()

Get metadata routing of this object.

get_params([deep])

Get parameters for this estimator.

set_fit_request(*[, cs_groups, cs_weights])

Configure whether metadata should be requested to be passed to the fit method.

set_params(**params)

Set the parameters of this estimator.

set_transform_request(*[, cs_groups, cs_weights])

Configure whether metadata should be requested to be passed to the transform method.

transform(X[, cs_weights, cs_groups])

Transform values into cross-sectional percentile ranks.

See also

CSGaussianRankScaler
CSStandardScaler

Examples

>>> import numpy as np
>>> from skfolio.preprocessing import CSPercentileRankScaler
>>>
>>> X = np.array([[1.0, np.nan, 3.0, 4.0],
...               [4.0, 3.0, 2.0, 1.0],
...               [10.0, 20.0, np.nan, 40.0]])
>>>
>>> transformer = CSPercentileRankScaler()
>>> transformer.fit_transform(X)
array([[0.16666667,        nan, 0.5       , 0.83333333],
       [0.875     , 0.625     , 0.375     , 0.125     ],
       [0.16666667, 0.5       ,        nan, 0.83333333]])
>>>
>>> # Restrict the estimation universe with cs_weights and rank within groups.
>>> cs_weights = np.array([[1.0, 0.0, 1.0, 1.0],
...                        [1.0, 0.0, 1.0, 1.0],
...                        [1.0, 1.0, 0.0, 1.0]])
>>> cs_groups = np.array([[0, 0, 1, 1],
...                       [0, 0, 1, 1],
...                       [0, 0, 1, 1]])
>>>
>>> transformer = CSPercentileRankScaler(min_group_size=2)
>>> transformer.fit_transform(X, cs_weights=cs_weights, cs_groups=cs_groups)
array([[0.16666667,        nan, 0.25      , 0.75      ],
       [0.83333333, 0.66666667, 0.75      , 0.25      ],
       [0.25      , 0.75      ,        nan, 0.83333333]])
fit(X, y=None, cs_weights=None, cs_groups=None)#

Fit the transformer.

Cross-sectional transformers are stateless and do not learn data-dependent parameters. This method validates the estimator parameters, validates X, and records n_features_in_ for scikit-learn compatibility.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset.

yIgnored

Not used, present for API consistency by convention.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional weights accepted for API consistency with transform. They are ignored during fitting.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional group labels accepted for API consistency with transform. They are ignored during fitting.

Returns:
selfBaseCSTransformer

Fitted estimator.

fit_transform(X, y=None, cs_weights=None, cs_groups=None)#

Fit to X and return the transformed values.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset.

yIgnored

Not used, present for API consistency by convention.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional weights forwarded to transform.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Optional cross-sectional group labels forwarded to transform.

Returns:
X_newndarray of shape (n_observations, n_assets)

Transformed array.

get_feature_names_out(input_features=None)#

Get output feature names for transformation.

Parameters:
input_featuresarray-like of str or None, default=None

Input features.

  • If input_features is None, then feature_names_in_ is used as feature names in. If feature_names_in_ is not defined, then the following input feature names are generated: ["x0", "x1", ..., "x(n_features_in_ - 1)"].

  • If input_features is an array-like, then input_features must match feature_names_in_ if feature_names_in_ is defined.

Returns:
feature_names_outndarray of str objects

Same as input features.

get_metadata_routing()#

Get metadata routing of this object.

Please check User Guide on how the routing mechanism works.

Returns:
routingMetadataRequest

A MetadataRequest encapsulating routing information.

get_params(deep=True)#

Get parameters for this estimator.

Parameters:
deepbool, default=True

If True, will return the parameters for this estimator and contained subobjects that are estimators.

Returns:
paramsdict

Parameter names mapped to their values.

set_fit_request(*, cs_groups='$UNCHANGED$', cs_weights='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the fit method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to fit if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to fit.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
cs_groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_groups parameter in fit.

cs_weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_weights parameter in fit.

Returns:
selfobject

The updated object.

set_params(**params)#

Set the parameters of this estimator.

The method works on simple estimators as well as on nested objects (such as Pipeline). The latter have parameters of the form <component>__<parameter> so that it’s possible to update each component of a nested object.

Parameters:
**paramsdict

Estimator parameters.

Returns:
selfestimator instance

Estimator instance.

set_transform_request(*, cs_groups='$UNCHANGED$', cs_weights='$UNCHANGED$')#

Configure whether metadata should be requested to be passed to the transform method.

Note that this method is only relevant when this estimator is used as a sub-estimator within a meta-estimator and metadata routing is enabled with enable_metadata_routing=True (see sklearn.set_config). Please check the User Guide on how the routing mechanism works.

The options for each parameter are:

  • True: metadata is requested, and passed to transform if provided. The request is ignored if metadata is not provided.

  • False: metadata is not requested and the meta-estimator will not pass it to transform.

  • None: metadata is not requested, and the meta-estimator will raise an error if the user provides it.

  • str: metadata should be passed to the meta-estimator with this given alias instead of the original name.

The default (sklearn.utils.metadata_routing.UNCHANGED) retains the existing request. This allows you to change the request for some parameters and not others.

Added in version 1.3.

Parameters:
cs_groupsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_groups parameter in transform.

cs_weightsstr, True, False, or None, default=sklearn.utils.metadata_routing.UNCHANGED

Metadata routing for cs_weights parameter in transform.

Returns:
selfobject

The updated object.

transform(X, cs_weights=None, cs_groups=None)[source]#

Transform values into cross-sectional percentile ranks.

Parameters:
Xarray-like of shape (n_observations, n_assets)

Input matrix where each row is an observation and each column is an asset. NaNs are allowed and preserved.

cs_weightsarray-like of shape (n_observations, n_assets), optional

Optional non-negative cross-sectional weights used only to define the estimation universe through the convention cs_weights > 0. Percentile ranks are then estimated in an equal-weighted way over the selected assets. Non-estimation assets still receive percentile ranks relative to that universe. If None, all finite assets are included in the estimation universe.

cs_groupsarray-like of shape (n_observations, n_assets), optional

Integer group labels >= -1. Missing groups (-1) and groups with fewer than min_group_size estimation assets fall back to the global cross-section. If None, ranking is performed globally within each observation.

Returns:
Pndarray of shape (n_observations, n_assets)

Percentile ranks in \([0.5 / N_{\mathcal{E}_t},\, 1 - 0.5 / N_{\mathcal{E}_t}]\), where \(N_{\mathcal{E}_t}\) is the size of the cross-section or group fallback used at observation \(t\). NaNs from X are preserved.

Raises:
ValueError

If min_group_size is not an integer >= 1, X is not a non-empty 2D array, cs_weights is invalid, cs_groups is invalid, or any observation has no estimation asset.